System and method for estimating a state vector associated with a battery

ABSTRACT

A system and a method for estimating a state vector associated with a battery are provided. The method includes determining a time interval that the battery has been electrically decoupled from a load circuit. The time interval starts at a first time. The method further includes obtaining a first state vector associated with the battery from a memory. The first state vector is determined prior to the first time. The method further includes calculating a second predicted state vector associated with the battery based on the first state vector and the time interval.

BACKGROUND OF THE INVENTION

Batteries are used in a wide variety of electronic and electricaldevices. It is desirable to be able to estimate the internal state of abattery, including state-of-charge (SOC). The SOC is a value thatindicates the present available capacity of the battery that may be usedto do work. A battery monitoring system may measure a history ofelectrical current input to a battery and an output voltage from thebattery to provide an estimate of a battery state.

A battery generally discharges over a time interval when it iselectrically unloaded and a state of the battery changes during thistime interval. A battery monitoring system, however, interrupts itshistory of measurements and calculations during this time interval. Adisadvantage of this system is that it cannot accurately determine thestate of the battery at a time when a battery is electrically coupled toa load circuit after it has been electrically decoupled from the loadcircuit for the time interval.

Thus, the inventor herein has recognized a need for a system and amethod for estimating a state vector associated with a battery at a timewhen a battery is electrically coupled to a load circuit, after it hasbeen electrically decoupled from the load circuit for the time interval.

BRIEF DESCRIPTION OF THE INVENTION

A method for estimating a state vector associated with a battery inaccordance with an exemplary embodiment is provided. The method includesdetermining a time interval that the battery has been electricallydecoupled from a load circuit. The time interval starts at a first time.The method further includes obtaining a first state vector associatedwith the battery from a memory. The first state vector is determinedprior to the first time. The method further includes calculating asecond predicted state vector associated with the battery based on thefirst state vector and the time interval. The method further includesmeasuring a battery voltage output by the battery to obtain a firstbattery voltage value after the first time interval when the battery iselectrically re-coupled to the load circuit. The method further includesestimating a second battery voltage value associated with the batterybased on the second predicted state vector. The method further includescalculating a voltage error value based on the first battery voltagevalue and the second battery voltage value. The method further includescalculating a third estimated state vector associated with the batterybased on the second predicted state vector and the voltage error value.

A system for estimating a state vector associated with a battery inaccordance with another exemplary embodiment is provided. The systemincludes a voltage sensor configured to measure a voltage output fromthe battery. The further includes a computer operably coupled to thevoltage sensor. The computer is configured to determine a time intervalthat the battery has been electrically decoupled from a load circuit.The time interval starts at a first time. The computer is furtherconfigured to obtain a first state vector associated with the batteryfrom a memory. The first state vector is determined prior to the firsttime. The computer is further configured to calculate a second predictedstate vector associated with the battery based on the first state vectorand the time interval. The computer is further configured to induce thevoltage sensor to measure the voltage output from the battery to obtaina first battery voltage value after the first time interval when thebattery is electrically coupled to the load circuit. The computer isfurther configured to estimate a second battery voltage value associatedwith the battery based on the second predicted state vector. Thecomputer is further configured to calculate a voltage error value basedon the first battery voltage value and the second battery voltage value.The computer is further configured to calculate a third estimated statevector associated with the battery based on the second predicted statevector and the voltage error value.

An article of manufacture in accordance with another exemplaryembodiment is provided. The article of manufacture includes a computerstorage medium having a computer program encoded therein for estimatinga state vector associated with a battery. The computer storage mediumincludes code for determining a time interval that the battery has beenelectrically decoupled from a load circuit. The time interval starts ata first time. The computer storage medium further includes code forobtaining a first state vector associated with the battery from amemory. The first state vector is determined prior to the first time.The computer storage medium further includes code for calculating asecond predicted state vector associated with the battery based on thefirst state vector and the time interval. The computer storage mediumfurther includes code for measuring a battery voltage output from thebattery to obtain a first battery voltage value after the first timeinterval when the battery is electrically coupled to the load circuit.The computer storage medium further includes code for estimating asecond battery voltage value associated with the battery based on thesecond predicted state vector. The computer storage medium furtherincludes code for calculating a voltage error value based on the firstbattery voltage value and the second battery voltage value. The computerstorage medium further includes code for calculating a third estimatedstate vector associated with the battery based on the second predictedstate vector and the voltage error value.

Other systems and/or methods according to the embodiments will become orare apparent to one with skill in the art upon review of the followingdrawings and detailed description. It is intended that all suchadditional systems and methods be within the scope of the presentinvention, and be protected by the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic of a system for estimating a state vectorassociated with a battery in accordance with an exemplary embodiment;

FIGS. 2-3 are flowcharts of a method for estimating a state vectorassociated with a battery in accordance with another exemplaryembodiment; and

FIGS. 4-5 are flowcharts of a method for estimating a state vectorassociated with a battery in accordance with another exemplaryembodiment.

DETAILED DESCRIPTION OF THE INVENTION

Referring to FIG. 1, a system 10 for estimating a state vectorassociated with a battery 12 is illustrated. The battery 12 includes atleast a battery cell 14. Of course, the battery 12 can include aplurality of additional battery cells. The system 10 includes one ormore voltage sensors 20, a load circuit 26, and a computational unitsuch as a computer 28, and may also include one or more of a temperaturesensor 22, and a current sensor 24.

The voltage sensor 20 is provided to generate a first output signalindicative of the voltage produced by one or more of the battery cellsof the battery 12. The voltage sensor 20 is electrically coupled betweenthe I/O interface 46 of the computer 28 and the battery 12. The voltagesensor 20 transfers the first output signal to the computer 28. Forclarity of presentation, a single voltage sensor will be describedherein. However, it should be noted that in an alternate embodiment ofsystem 10 a plurality of voltage sensors (e.g., one voltage sensor perbattery cell) are utilized in system 10.

The temperature sensor 22 is provided to generate a second output signalindicative of one or more temperatures of the battery 12. Thetemperature sensor 22 is disposed proximate the battery 12 and iselectrically coupled to the I/O interface 46 of the computer 28. Thetemperature sensor 22 transfers the second output signal to the computer28. For clarity of presentation, a single temperature sensor will bedescribed herein. However, it should be noted that in an alternateembodiment of system 10 a plurality of temperature sensors (e.g., onetemperature sensor per battery cell) are utilized in system 10.

The current sensor 24 is provided to generate a third output signalindicative of a current sourced or sunk by the battery cells of thebattery 12. The current sensor 24 is electrically coupled between thebattery 12 and the load circuit 26. The current sensor 24 is furtherelectrically coupled to the I/O interface 46 of the computer 28. Thecurrent sensor 24 transfers the third output signal to the computer 28.

The load circuit 26 is electrically coupled to the current sensor 24 andsinks or sources a current from the battery 12. The load circuit 26comprises any electrical device that can be electrically coupled to thebattery 12.

The computer 28 is provided for determining a state vector associatedwith the battery 12, as will be explained in greater detail below. Thecomputer 28 includes a central processing unit (CPU) 40, a read-onlymemory (ROM) 44, a volatile memory such as a random access memory (RAM)45 and an input/output (I/O) interface 46. The CPU 40 operablycommunicates with the ROM 44, the RAM 45, and the I/O interface 46. TheCPU 40 includes a clock 42. The computer readable media including ROM 44and RAM 46 may be implemented using any of a number of known memorydevices such as PROMs, EPROMs, EEPROMS, flash memory or any otherelectric, magnetic, optical or combination memory device capable ofstoring data, some of which represent executable instructions used bythe CPU 40.

Before providing a detailed discussion of the methodologies fordetermining a state vector associated with the battery 12, a generaloverview will be provided. The state vector includes at least a state ofcharge (SOC) value associated with the battery 12. The SOC value is avalue from 0-100 percent, that indicates a present available capacity ofthe battery 12 that may be used to do work. The estimated state vectoris determined when the load circuit 26 is energized utilizing thefollowing parameters: (i) measured battery voltage (ii) a stored priorestimated state vector (including an SOC value); and (iii) a timeinterval that the load circuit 12 was de-energized or electricallyde-coupled from the batter 12. These parameters are utilized in amathematical model of battery cell behavior in order to compute animproved estimate of the state vector of the battery 12 includingpossibly compensation for hysteresis effects, voltage polarizationeffects, and self-discharge. The duration of time that the device wasde-energized may be measured using the clock 42 of the computer 28.

It is assumed that a mathematical model of the battery cell dynamics isknown, and may be expressed using a discrete-time state-space modelcomprising a state equation and an output equation, as will be describedbelow.

The state equation utilized to determine the state vector associatedwith the battery 12 is as follows:x _(k) =f(x _(k−1) ,u _(k−1) ,w _(k−1) ,k−1,k)wherein,x_(k) is the state vector associated with the battery 12 at time indexk;u_(k) is a variable representing a known/deterministic input to thebattery 12;w_(k) is a process noise or disturbance that models some unmeasuredinput which affects the state of the system; andf(x_(k−1),u_(k−1),w_(k−1),k−1,k) is a state transition function.The state vector x_(k) includes a SOC value therein. Further, theknown/deterministic input u_(k) includes at least one of: (i) anelectrical current presently sourced or sunk by the battery 12, and (ii)a temperature of the battery 12.

An output vector associated with the battery 12 is determined utilizingthe following equation:y _(k=h)(x _(k) ,u _(k) ,v _(k) ,k)wherein,h(x_(k),u_(k),v_(k),k) is a measurement function; andv_(k) is sensor noise that affects the measurement of the output ofbattery 12 in a memory-less mode, but does not affect the state vectorof battery 12.

The following system utilizes probabilistic inference to determine anestimated state vector {circumflex over (x)}_(k) of the state vectorx_(k) given all observations Y_(k)={y₀,y₁, . . . ,y_(k)}. A frequentlyused estimator is the conditional mean:x̂_(k) = E[x_(k)|Y_(k)] = ∫_(R_(x_(k)))x_(k)p(x_(k)|Y_(k))𝕕x_(k)where R_(x) _(k) is the range of x_(k), and E[ ] is the statisticalexpectation operator. The foregoing equation computes a posteriorprobability density p(x_(k)|Y_(k)) recursively. Because the foregoingequation is difficult to solve, numerical methods have been utilized toapproximate the equation to calculate the estimated state vector{circumflex over (x)}_(k), as will be explained in greater detail below.

For purposes of understanding, the notation utilized in the equations ofthe following methods will be described. The circumflex symbol indicatesan estimated quantity (e.g., {circle around (x)} indicates an estimateof the true quantity x). The superscript symbol “−” indicates an apriori estimate (i.e., a prediction of a quantity's present value basedon past data). The superscript symbol “+” indicates an a posterioriestimate (e.g., {circle around (x)}_(k) ⁺ is the estimate of truequantity x at time index k based on all measurements taken up to andincluding time k). The tilde symbol indicates the error of an estimatedquantity (e.g., {tilde over (x)}_(k) ⁻=x_(k)−{circumflex over (x)}_(k) ⁻and {tilde over (x)}_(k) ⁺=x_(k)−{circumflex over (x)}_(k) ⁺). Thesymbol Σ_(xy)=E[xy^(T)] indicates the correlation or cross correlationof the variables in its subscript (the quantities described herein arezero-mean, so the correlations are identical to covariances). The symbolΣ_(X) indicates the same quantity as Σ_(xx). The superscript “T” is amatrix/vector transpose operator.

Referring to FIGS. 2-3, a method for calculating the estimated statevector {circumflex over (x)}_(k) utilizing a general sequentialprobabilistic inference methodology will be explained.

At step 60, the computer 28 determines a time interval that the battery12 has been electrically decoupled from the load circuit 26. The timeinterval starts at a first time.

At step 62, the computer 28 obtains a first state vector x_(k−1)associated with the battery 12 from a memory 46. The first state vectorx_(k−1) is determined prior to the first time.

At step 64, the computer 28 calculates a second predicted state vector{circumflex over (x)}_(k) ⁻ associated with the battery 12 based on thefirst state vector x_(k−1) and the time interval, utilizing theequation: {circumflex over (x)}_(k) ⁻=E[f(x_(k−1), u_(k−1), w_(k−1),k−1)|Y_(k−1)].

At step 66, the computer 28 calculates a first covariance valueΣ_({tilde over (x)},k) ⁻ associated with the second predicted statevector {circumflex over (x)}_(k) ⁻, utilizing the equation:Σ_({tilde over (x)},k) ⁻ =E[({tilde over (x)}_(k) ⁻)({tilde over(x)}_(k) ⁻)^(T)].

At step 68, the computer 28 induces a voltage sensor 20 to measure abattery voltage output by the battery 12 to obtain a first batteryvoltage value after the first time interval when the battery 12 iselectrically coupled to the load circuit 26.

At step 70, the computer 28 estimates a second battery voltage valueassociated with the battery 12 based on the second predicted statevector {circumflex over (x)}_(k) ⁻, utilizing the equation:ŷ_(k) =E[h(x _(k) ,u _(k),v_(k) ,k)|Y _(k)].

At step 72, the computer 28 calculates a voltage error value based onthe first battery voltage value and the second battery voltage value.

At step 74, the computer 28 calculates a third predicted state vector{tilde over (x)}_(k) ⁺ associated with the battery 12 based on thesecond predicted state vector {circumflex over (x)}_(k) ⁻ and thevoltage error value y_(k)−ŷ_(k), utilizing the equation: {tilde over(x)}_(k) ⁺={tilde over (x)}_(k) ⁻+L_(k)[y_(k)−{tilde over (y)}_(k)].where the value L_(k) is determined utilizing the equation:L _(k) =E[({tilde over (x)}_(k) ⁻){tilde over (y)}_(k))^(T)](E[({tildeover (y)}_(k))({tilde over (y)}_(k))^(T)])⁻¹The third predicted state vector {circumflex over (x)}_(k) ⁺ is the mostaccurate estimate of the true state of the battery 12 produced by theforegoing method.

At step 76, the computer 28 calculates a second covariance valueΣ_({tilde over (x)},k) ⁺ associated with the third predicted statevector {circumflex over (x)}_(k) ⁺, utilizing the equation:

Σ_({tilde over (x)},k) ⁺=Σ_({tilde over (x)},k)⁻−L_(k)Σ_({tilde over (y)},k)L_(k) ^(T). After step 76, the method isexited.

It should be noted that there are many methods for approximating thethird predicted state vector {circumflex over (x)}_(k) ⁺, describedabove. For example, one method utilizes a linear Kalman filter, anothermethod utilizes a nonlinear extended Kalman filter, another methodutilizes a nonlinear sigma-point Kalman filter. These methods utilizedifferent levels of computational effort and produce different degreesof accuracy in the resulting state vector.

For example, referring to FIGS. 4-5, a method for calculating theestimated state vector {circumflex over (x)}_(k) utilizing a non-linearextended Kalman filter will be explained. The following definitions areutilized in the equations of the method: $\begin{matrix}{{\hat{A}}_{k} = {\frac{\partial{f\left( {x_{k},u_{k},w_{k},k} \right)}}{\partial x_{k}}❘_{x_{k} = {\hat{x}}_{k}^{+}}}} & {{\hat{B}}_{k} = {\frac{\partial{f\left( {x_{k},u_{k},w_{k},k} \right)}}{\partial w_{k}}❘_{w_{k} = {\overset{\_}{w}}_{k}}}} \\{{\hat{C}}_{k} = {\frac{\partial{h\left( {x_{k},u_{k},v_{k},k} \right)}}{\partial x_{k}}❘_{x_{k} = {\hat{x}}_{k}^{-}}}} & {{\hat{D}}_{k} = {\frac{\partial{h\left( {x_{k},u_{k},v_{k},k} \right)}}{\partial v_{k}}❘_{v_{k} = {\overset{\_}{v}}_{k}}}}\end{matrix}$

At step 80, the computer 28 determines a time interval that a battery 12has been electrically decoupled from a load circuit 26. The timeinterval starts at a first time.

At step 82, the computer 28 obtains a first state vector {circumflexover (x)}_(k−1) ⁺ associated with the battery 12 from the memory 46. Thefirst state vector {circumflex over (x)}_(k−1) ⁺ is determined prior tothe first time.

At step 84, the computer 28 calculates a second predicted state vector{circumflex over (x)}_(k) ⁻ associated with the battery 12 based on thefirst state vector {circumflex over (x)}_(k−1) ⁺ and the time interval,utilizing the equation: {circumflex over (x)}_(k) ⁻=f({circumflex over(x)}_(k−1) ⁺,u_(k−1), w _(k−1),k−1,k).

At step 86, the computer 28 calculates a first covariance valueΣ_({tilde over (x)},k) ⁻ associated with the second predicted statevector {circumflex over (x)}_(k) ⁻, utilizing the equation:Σ_({tilde over (x)},k) ⁻ =Â _(k−1)Σ_({tilde over (x)},k−1) ⁺ Â _(k−1)^(T) +{circumflex over (B)}k−1 Σ_(w) {circumflex over (B)} _(k−1) ^(T).

At step 88, the computer 28 induces the voltage sensor 20 to measure abattery voltage output by the battery 12 to obtain a first batteryvoltage value after the first time interval when the battery 12 iselectrically coupled to the load circuit 26.

At step 90, the computer 28 estimates a second battery voltage valueassociated with the battery 12 based on the second predicted statevector {tilde over (x)}_(k) ⁻, utilizing the equation:ŷ_(k) =h({circumflex over (x)}_(k) ⁻ ,u _(k) ,{tilde over (v)} _(k) ,k).

At step 92, the computer 28 calculates a voltage error value based onthe first battery voltage value and the second battery voltage value.

At step 94, the computer 28 calculates a third predicted state vector{circumflex over (x)}_(k) ⁺ associated with the battery 12 based on thesecond predicted state vector {circumflex over (x)}_(k) ⁻ and thevoltage error value, utilizing the equation: {circumflex over (x)}_(k)⁺={circumflex over (x)}_(k) ⁻+L_(k)[y_(k)−ŷ_(k)]. where the value L_(k)is determined utilizing the equation:L _(k)=Σ_({tilde over (x)},k) ⁻ Ĉ _(k) ^(T) [Ĉ_(k)Σ_({tilde over (x)},k) Ĉ _(k) ^(T) +{circumflex over (D)} _(k)Σ_(v){circumflex over (D)} _(k) ^(T)]⁻¹.

At step 96, the computer 28 calculates a second covariance valueΣ_({tilde over (x)},k) ⁺ associated with the third predicted statevector {circumflex over (x)}_(k) ⁺, utilizing the equation:

Σ_({circumflex over (x)},k) ⁺=(I−L_(k)Ĉ_(k))Σ_({tilde over (x)},k) ⁻.After step 96, the method is exited.

It should be noted that in alternate embodiments, the estimated statevector {circumflex over (x)}_(k) of the battery 12 can be calculatedutilizing a linear Kalman filter, a nonlinear sigma-point Kalman filter,a square-root linear Kalman filter, a square-root extended Kalmanfilter, a square-root sigma-point Kalman filter, a particle filter, andthe like.

The system and methods for estimating a state vector associated with abattery provide a substantial advantage over other systems and methods.In particular, the system and methods provide a technical effect ofaccurately estimating a state vector associated with the battery at atime when a battery is electrically coupled to a load circuit, after ithas been electrically decoupled from the load circuit for the timeinterval.

The above-described methods can be embodied in the form of computerprogram code containing instructions embodied in tangible media, such asfloppy diskettes, CD ROMs, hard drives, or any other computer-readablestorage medium, wherein, when the computer program code is loaded intoand executed by a computer, the computer becomes an apparatus forpracticing the invention. The above-described methods can also beembodied in the form of computer program code, for example, whetherstored in a storage medium, loaded into and/or executed by a computer,or transmitted over some transmission medium, loaded into and/orexecuted by a computer, or transmitted over some transmission medium,such as over electrical wiring or cabling, through fiber optics, or viaelectromagnetic radiation, wherein, when the computer program code isloaded into an executed by a computer, the computer becomes an apparatusfor practicing the methods. When implemented on a general-purposemicroprocessor, the computer program code segments configure themicroprocessor to create specific logic circuits.

While the invention is described with reference to the exemplaryembodiments, it will be understood by those skilled in the art thatvarious changes may be made an equivalence may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications may be made to the teachings of theinvention to adapt to a particular situation without departing from thescope thereof. Therefore, is intended that the invention not be limitedthe embodiment disclosed for carrying out this invention, but that theinvention includes all embodiments falling with the scope of theintended claims. Moreover, the use of the term's first, second, etc.does not denote any order of importance, but rather the term's first,second, etc. are used to distinguish one element from another.

1. A method for estimating a state vector associated with a battery, themethod comprising: determining a time interval that the battery has beenelectrically decoupled from a load circuit, the time interval startingat a first time; obtaining a first state vector associated with thebattery from a memory, the first state vector being determined prior tothe first time; calculating a second predicted state vector associatedwith the battery based on the first state vector and the time interval;measuring a battery voltage output by the battery to obtain a firstbattery voltage value after the first time interval when the battery iselectrically re-coupled to the load circuit; estimating a second batteryvoltage value associated with the battery based on the second predictedstate vector; calculating a voltage error value based on the firstbattery voltage value and the second battery voltage value; andcalculating a third estimated state vector associated with the batterybased on the second predicted state vector and the voltage error value.2. The method of claim 1, further comprising calculating a covariancevalue associated with the third estimated state vector.
 3. The method ofclaim 1, wherein the step of calculating the second predicted statevector comprises calculating the second predicted state vectorassociated with the battery based on the first state vector and the timeinterval utilizing at least one of a Kalman filter, an extended Kalmanfilter, a sigma-point Kalman filter, a square-root sigma-point Kalmanfilter, and a particle filter.
 4. The method of claim 1, wherein thestep of calculating the third estimated state vector comprisescalculating the third estimate state vector associated with the batterybased on the second predicted state vector and the voltage error valueinterval utilizing at least one of a Kalman filter, an extended Kalmanfilter, a sigma-point Kalman filter, a square-root sigma-point Kalmanfilter, and a particle filter.
 5. The method of claim 1, wherein thesecond predicted state vector is indicative of at least a predictedstate-of-charge of the battery.
 6. The method of claim 1, wherein thethird predicted state vector is indicative of at least a predictedstate-of-charge of the battery.
 7. A system for estimating a statevector associated with a battery, the system comprising: a voltagesensor configured to measure a voltage output from the battery; and acomputer operably coupled to the voltage sensor, the computer configuredto determine a time interval that the battery has been electricallydecoupled from a load circuit, the time interval starting at a firsttime, the computer further configured to obtain a first state vectorassociated with the battery from a memory, the first state vector beingdetermined prior to the first time, the computer further configured tocalculate a second predicted state vector associated with the batterybased on the first state vector and the time interval, the computerfurther configured to induce the voltage sensor to measure the voltageoutput from the battery to obtain a first battery voltage value afterthe first time interval when the battery is electrically coupled to theload circuit, the computer further configured to estimate a secondbattery voltage value associated with the battery based on the secondpredicted state vector, the computer further configured to calculate avoltage error value based on the first battery voltage value and thesecond battery voltage value, the computer further configured tocalculate a third estimated state vector associated with the batterybased on the second predicted state vector and the voltage error value.8. The system of claim 7, further comprising calculating a covariancevalue associated with the third estimated state vector.
 9. The system ofclaim 7, wherein the computer is further configured to calculate thesecond predicted state vector associated with the battery based on thefirst state vector and the time interval, utilizing at least one of aKalman filter, an extended Kalman filter, a sigma-point Kalman filter, asquare-root Kalman filter, a square-root extended Kalman filter, asquare-root sigma-point Kalman filter, and a particle filter.
 10. Thesystem of claim 7, wherein the computer is further configured tocalculate the third estimated state vector associated with the batterybased on the second predicted state vector, the first measured batteryvoltage, and the second estimated battery voltage, utilizing at leastone of a Kalman filter, an extended Kalman filter, a sigma-point Kalmanfilter, a square-root Kalman filter, a square-root extended Kalmanfilter, a square-root sigma-point Kalman filter, and a particle filter.11. The system of claim 7, wherein the second predicted state vector isindicative of at least a predicted state-of-charge of the battery. 12.The system of claim 7, wherein the third estimated state vector isindicative of at least a predicted state-of-charge of the battery. 13.An article of manufacture, comprising: a computer storage medium havinga computer program encoded therein for estimating a state vectorassociated with a battery, the computer storage medium comprising: codefor determining a time interval that the battery has been electricallydecoupled from a load circuit, the time interval starting at a firsttime; code for obtaining a first state vector associated with thebattery from a memory, the first state vector being determined prior tothe first time; code for calculating a second predicted state vectorassociated with the battery based on the first state vector and the timeinterval; code for measuring a battery voltage output from the batteryto obtain a first battery voltage value after the first time intervalwhen the battery is electrically coupled to the load circuit; code forestimating a second battery voltage value associated with the batterybased on the second predicted state vector; code for calculating avoltage error value based on the first battery voltage value and thesecond battery voltage value; and code for calculating a third estimatedstate vector associated with the battery based on the second predictedstate vector and the voltage error value.
 14. The article of manufactureof claim 13, wherein the computer storage medium further comprises codefor calculating a covariance value associated with the third estimatedstate vector.
 15. The article of manufacture of claim 13, wherein thecode for calculating the second predicted state vector comprises codefor calculating the second predicted state vector based on the firststate vector and the time interval utilizing at least one of a Kalmanfilter, an extended Kalman filter, a sigma-point Kalman filter, asquare-root Kalman filter, a square-root extended Kalman filter, asquare-root sigma-point Kalman filter, and a particle filter.
 16. Thearticle of manufacture of claim 13, wherein the code for calculating thethird estimated state vector comprises code for calculating the thirdestimated state vector based on the second predicted state vector, thefirst measured battery voltage and the second estimated battery voltageutilizing at least one of a Kalman filter, an extended Kalman filter, asigma-point Kalman filter, a square-root Kalman filter, a square-rootextended Kalman filter, a square-root sigma-point Kalman filter, and aparticle filter.
 17. The article of manufacture of claim 13, wherein thesecond predicted state vector is indicative of at least a predictedstate-of-charge of the battery.
 18. The article of manufacture of claim13, wherein the third estimated state vector is indicative of at least apredicted state-of-charge of the battery.